Objective： To investigate the relationship between the lipid metabolism-related gene sterol carrier protein 2(SCP2) and prostate cancer (PCa) from a multi-omics perspective using single-cell transcriptomes combined with Mendelian randomization. Methods： Single-cell transcriptome data of benign and malignant prostate tissues were obtained from GSE120716, GSE157703 and GSE141445 datasets, respectively. Integration, quality control and annotation were performed on the data to categorize the epithelial cells into high and low SCP2 expression groups, followed by further differential and trajectory analyses. Single nucleotide polymorphism (SNP) data for SCP2 expression quantitative trait loci (eQTL) were subsequently downloaded from Genotype-Tissue Expression (GTEx) and investigated from the PCa Society Cancer-Related Genomic Alteration Panel for the Investigation of Cancer-Related Alterations (PRACTICAL) to obtain PCa outcome data for Mendelian randomization analysis to validate the causal relationship between SCP2 and PCa. Results： High SCP2-expressing epithelial cells had higher energy metabolism and proliferation capacity with low immunotherapy response and metastatic tendency. Trajectory analysis showed that epithelial cells with high SCP2 expression may have a higher degree of malignancy, and SCP2 may be a key marker gene for differentiation of malignant epithelial cells in the prostate. Further Mendelian randomization results showed a significant causal relationship between SCP2 and PCa development (OR=1.045, 95% CI: 1.010 －1.083, P=0.011). Conclusion： By combining single-cell transcriptome and Mendelian randomization, the role of the lipid metabolism-related gene SCP2 in PCa development has been confirmed, and new targets and therapeutic directions for PCa treatment have been provided.
Humans ; Prostatic Neoplasms/genetics* ; Male ; Mendelian Randomization Analysis ; Polymorphism, Single Nucleotide ; Quantitative Trait Loci ; Single-Cell Analysis ; Sequence Analysis, RNA ; Carrier Proteins/genetics* ; Transcriptome ; Lipid Metabolism

Rice (Oryza sativa L.), as a thermophilic crop, is highly susceptible to cold stress during its growth process. Chilling injury at the plumule stage and seedling stage often affects the morphological development and leads to yield reduction of rice. The exploration and utilization of cold tolerance genes are among the most direct and effective approaches to address cold stress in rice. To identify quantitative trait loci (QTLs) associated with cold tolerance at plumule and seedling stages, in this study, we measured the seedling rates and survived seedling rates of the indica rice cultivar 'HZ', the japonica cultivar 'Nekken2', and their 120 recombinant inbred lines (RILs) under cold stress. A previously constructed high-density genetic linkage map was used for the mapping of the QTLs conferring cold tolerance at the plumule and seedling stages. A total of 4 QTLs for plumule-stage cold tolerance and 9 QTLs for seedling-stage cold tolerance were detected, with the maximum limit of detection reaching 5.20. Notably, a genetically overlapping QTL for both plumule and seedling stages was identified on chromosome 8, spanning a physical interval of 24 432 953-25 295 129 bp. Candidate genes within the detected QTL intervals were screened, and quantitative real-time polymerase chain reaction (qRT-PCR) was conducted to analyze the gene expression during the plumule and seedling stages. The results revealed that LOC_Os03g06570, LOC_Os03g07100, LOC_Os06g08280, LOC_Os08g38440, LOC_Os08g39100, and LOC_Os08g39540 exhibited significantly differential expression between the parental lines. These genes were either significantly downregulated or upregulated under cold stress. Among them, the first three gene (LOC_Os03g06570, LOC_Os03g07100, and LOC_Os06g08280) were hypothesized to be key candidates regulating the cold tolerance of rice seedlings, while the latter three genes (LOC_Os08g38440, LOC_Os08g39100, and LOC_Os08g39540) were identified as comprehensive regulators of cold tolerance during both plumule and seedling stages. These findings lay a foundation for the fine mapping and cloning of cold tolerance genes at the plumule and seedling stages, providing valuable insights for breeding cold-tolerant rice varieties.
Quantitative Trait Loci/genetics* ; Oryza/growth & development* ; Seedlings/growth & development* ; Cold Temperature ; Chromosome Mapping ; Gene Expression Regulation, Plant

Excavating the quantitative trait locus (QTL) associated with rice cooking quality, analyzing candidate genes, and improving cooking quality-associated traits of rice varieties by genetic breeding can effectively improve the taste of rice. In this study, we used the indica rice HZ, the japonica rice Nekken2 and 120 recombinant inbred lines (RILs) populations constructed from them as experimental materials to measure the gelatinization temperature (GT), gel consistency (GC) and amylose content (AC) of rice at the maturity stage. We combined the high-density genetic map for QTL mapping. A total of 26 QTLs associated with rice cooking quality (1 QTL associated with GT, 13 QTLs associated with GC, and 12 QTLs associated with AC) were detected, among which the highest likelihood of odd (LOD) value reached 30.24. The expression levels of candidate genes in the localization interval were analyzed by quantitative real-time polymerase chain reaction (qRT-PCR), and it was found that the expression levels of six genes were significantly different from that in parents. It was speculated that the high expression of LOC_Os04g20270 and LOC_Os11g40100 may greatly increase the GC of rice, while the high expression of LOC_Os01g04920 and LOC_Os02g17500 and the low expression of LOC_Os03g02650 and LOC_Os05g25840 may reduce the AC. The results lay a molecular foundation for the cultivation of new high-quality rice varieties, and provide important genetic resources for revealing the molecular regulation mechanism of rice cooking quality.
Quantitative Trait Loci ; Oryza/genetics* ; Plant Breeding ; Cooking ; Genetic Association Studies

OBJECTIVE: N6-methyladenosine (m ⁶A) is a common epigenetic modification in eukaryotes. In this study, we explore the potential impact of m ⁶A-associated single nucleotide polymorphisms (m ⁶A-SNPs) on heart failure (HF). METHODS: Data from genome-wide association studies (GWAS) investigating HF in humans and from m ⁶A-SNPs datasets were used to identify HF-associated m ⁶A-SNPs. Their functions were explored using expression quantitative trait locus (eQTL), gene expression, and gene enrichment analyses. Mediation protein quantitative trait locus (pQTL)-Mendelian randomization (MR) was used to investigate the potential mechanism between critical protein levels and risk factors for HF. RESULTS: We screened 44 HF-associated m ⁶A-SNPs, including 10 m ⁶A-SNPs that showed eQTL signals and differential expressions in HF. The SNP rs1801270 in CDKN1A showed the strongest association with HF ( P = 7.75 × 10 ^-6). Additionally, MR verified the genetic association between the CDKN1A protein and HF, as well as the mediating effect of blood pressure (BP) in this pathway. Higher circulating level of CDKN1A was associated with a lower risk of HF (odds ratio [ OR] = 0.82, 95% confidence interval [ CI]: 0.69 to 0.99). The proportions of hypertension, systolic BP, and diastolic BP were 48.10%, 28.94%, and 18.02%, respectively. Associations of PDIA6 ( P = 1.30 × 10 ^-2) and SMAD3 ( P = 4.80 × 10 ^-2) with HF were also detected. CONCLUSION Multiple HF-related m ⁶A-SNPs were identified in this study. Genetic associations of CDKN1A and other proteins with HF and its risk factors were demonstrated, providing new ideas for further exploration of the molecular mechanisms of HF.
Humans ; Polymorphism, Single Nucleotide ; Heart Failure/genetics* ; Mendelian Randomization Analysis ; Genome-Wide Association Study ; Cyclin-Dependent Kinase Inhibitor p21/metabolism* ; Quantitative Trait Loci ; Adenosine/metabolism* ; Male ; Female ; Genetic Predisposition to Disease

The chemical characterization analysis of a medical device often results in chemical substances with unknown toxicities. While identification of each individual toxicity could result in a time-consuming hurdle with tremendous labor and financial burden, quantitative structure-activity relationship (QSAR) is of great significance for toxicity risk assessment of such chemical substances. By establishing quantitative relationship between the molecular structures or active groups of similar chemical compounds with their biological activities, QSAR can be utilized to predict the toxicity of such target compounds with significantly reduced cost and time. In this article, the authors generally summarized the mechanisms of QSAR approaches, current applications of QSAR modeling in the field of medical device, an introduction of the characteristics of publicly and commercially-available QSAR software, and briefly explored future trends of QSAR modeling in medical device toxicological risk assessment. The utilization of QSAR would undoubtedly further advance the toxicological risk assessment of medical devices.
Quantitative Structure-Activity Relationship ; Risk Assessment ; Software

Cancer is a complex multifaceted illness that affects different patients in discrete ways. For a number of cancers the use of chemotherapy has become standard practice. Chemotherapy is a use of cytostatic drugs to cure cancer. Cytostatic agents not only affect cancer cells but also affect the growth of normal cells; leading to side effects. Because of this, radiotherapy gained importance in treating cancer. Slaughtering of cancerous cells by radiotherapy depends on the radiosensitivity of the tumor cells. Efforts to improve the therapeutic ratio have resulted in the development of compounds that increase the radiosensitivity of tumor cells or protect the normal cells from the effects of radiation. Amifostine is the only chemical radioprotector approved by the US Food and Drug Administration (FDA), but due to its side effect and toxicity, use of this compound was also failed. Hence the use of herbal radioprotectors bearing pharmacological properties is concentrated due to their low toxicity and efficacy. Notably, in silico methods can expedite drug discovery process, to lessen the compounds with unfavorable pharmacological properties at an early stage of drug development. Hence a detailed perspective of these properties, in accordance with their prediction and measurement, are pivotal for a successful identification of radioprotectors by drug discovery process.
Amifostine ; Computer Simulation ; Cytostatic Agents ; Drug Discovery ; Drug Therapy ; Humans ; Quantitative Structure-Activity Relationship ; Radiation Tolerance ; Radiotherapy ; United States Food and Drug Administration

OBJECTIVES: The molecular mechanisms underlying attention-deficit hyperactivity disorder (ADHD) remain unclear. Therefore, this study aimed to identify the genetic susceptibility loci for ADHD in Korean children with ADHD. We performed a case-control and a family-based genome-wide association study (GWAS), as well as genome-wide quantitative trait locus (QTL) analyses, for two symptom traits. METHODS: A total of 135 subjects (71 cases and 64 controls), for the case-control analysis, and 54 subjects (27 probands and 27 unaffected siblings), for the family-based analysis, were included. RESULTS: The genome-wide QTL analysis identified four single nucleotide polymorphisms (SNPs) (rs7684645 near APELA, rs12538843 near YAE1D1 and POU6F2, rs11074258 near MCTP2, and rs34396552 near CIDEA) that were significantly associated with the number of inattention symptoms in ADHD. These SNPs showed possible association with ADHD in the family-based GWAS, and with hyperactivity-impulsivity in genome-wide QTL analyses. Moreover, association signals in the family-based QTL analysis for the number of inattention symptoms were clustered near genes IL10, IL19, SCL5A9, and SKINTL. CONCLUSION: We have identified four QTLs with genome-wide significance and several promising candidates that could potentially be associated with ADHD (CXCR4, UPF1, SETD5, NALCN-AS1, ERC1, SOX2-OT, FGFR2, ANO4, and TBL1XR1). Further replication studies with larger sample sizes are needed.
Adolescent* ; Case-Control Studies ; Child ; Genetic Predisposition to Disease ; Genome-Wide Association Study ; Humans ; Interleukin-10 ; Polymorphism, Single Nucleotide ; Quantitative Trait Loci ; Sample Size

Although pork quality traits are important commercially, genome-wide association studies (GWASs) have not well considered Landrace and Yorkshire pigs worldwide. Landrace and Yorkshire pigs are important pork-providing breeds. Although quantitative trait loci of pigs are well-developed, significant genes in GWASs of pigs in Korea must be studied. Through a GWAS using the PLINK program, study of the significant genes in Korean pigs was performed. We conducted a GWAS and surveyed the gene ontology (GO) terms associated with the backfat thickness (BF) trait of these pigs. We included the breed information (Yorkshire and Landrace pigs) as a covariate. The significant genes after false discovery rate (<0.01) correction were AFG1L, SCAI, RIMS1, and SPDEF. The major GO terms for the top 5% of genes were related to neuronal genes, cell morphogenesis and actin cytoskeleton organization. The neuronal genes were previously reported as being associated with backfat thickness. However, the genes in our results were novel, and they included ZNF280D, BAIAP2, LRTM2, GABRA5, PCDH15, HERC1, DTNBP1, SLIT2, TRAPPC9, NGFR, APBB2, RBPJ, and ABL2. These novel genes might have roles in important cellular and physiological functions related to BF accumulation. The genes related to cell morphogenesis were NOX4, MKLN1, ZNF280D, BAIAP2, DNAAF1, LRTM2, PCDH15, NGFR, RBPJ, MYH9, APBB2, DTNBP1, TRIM62, and SLIT2. The genes that belonged to actin cytoskeleton organization were MKLN1, BAIAP2, PCDH15, BCAS3, MYH9, DTNBP1, ABL2, ADD2, and SLIT2.
Actin Cytoskeleton ; Gene Ontology ; Genome-Wide Association Study* ; Korea ; Morphogenesis ; Neurons ; Quantitative Trait Loci ; Red Meat ; Swine*

The alteration of alternative splicing patterns has an effect on the quantification of functional proteins, leading to phenotype variation. The splicing quantitative trait locus (sQTL) is one of the main genetic elements affecting splicing patterns. Here, we report the results of genome-wide sQTLs across 141 strains of Arabidopsis thaliana with publicly available next generation sequencing datasets. As a result, we found 1,694 candidate sQTLs in Arabidopsis thaliana at a false discovery rate of 0.01. Furthermore, among the candidate sQTLs, we found 25 sQTLs that overlapped with the list of previously examined trait-associated single nucleotide polymorphisms (SNPs). In summary, this sQTL analysis provides new insight into genetic elements affecting alternative splicing patterns in Arabidopsis thaliana and the mechanism of previously reported trait-associated SNPs.
Alternative Splicing ; Arabidopsis* ; Dataset ; Phenotype ; Polymorphism, Single Nucleotide ; Quantitative Trait Loci*

The human pregnane X receptor (hPXR) plays a critical role in the metabolism, transport and clearance of xenobiotics in the liver and intestine. The hPXR can be activated by a structurally diverse of drugs to initiate clinically relevant drug-drug interactions. In this article, in silico investigation was performed on a structurally diverse set of drugs to identify critical structural features greatly related to their agonist activity towards hPXR. Heuristic method (HM)-Best Subset Modeling (BSM) and HM-Polynomial Neural Networks (PNN) were utilized to develop the linear and non-linear quantitative structure-activity relationship models. The applicability domain (AD) of the models was assessed by Williams plot. Statistically reliable models with good predictive power and explain were achieved (for HM-BSM, r (2)=0.881, q LOO (2) =0.797, q EXT (2) =0.674; for HM-PNN, r (2)=0.882, q LOO (2) =0.856, q EXT (2) =0.655). The developed models indicated that molecular aromatic and electric property, molecular weight and complexity may govern agonist activity of a structurally diverse set of drugs to hPXR.
Computer Simulation ; Humans ; Models, Statistical ; Molecular Weight ; Neural Networks (Computer) ; Quantitative Structure-Activity Relationship ; Receptors, Steroid ; agonists ; chemistry ; Small Molecule Libraries ; chemistry ; Static Electricity